Feed Efficiency In Dairy Cattle Research Articles

Unraveling the Genetic Basis of Feed Efficiency in Cattle through Integrated DNA Methylation and CattleGTEx Analysis.

Feed costs can amount to 75 percent of the total overhead cost of raising cows for milk production. Meanwhile, the livestock industry is considered a significant contributor to global climate change due to the production of greenhouse gas emissions, such as methane. Indeed, the genetic basis of feed efficiency (FE) is of great interest to the animal research community. Here, we explore the epigenetic basis of FE to provide base knowledge for the development of genomic tools to improve FE in cattle. The methylation level of 37,554 CpG sites was quantified using a mammalian methylation array (HorvathMammalMethylChip40) for 48 Holstein cows with extreme residual feed intake (RFI). We identified 421 CpG sites related to 287 genes that were associated with RFI, several of which were previously associated with feeding or digestion issues. Activator of transcription and developmental regulation (AUTS2) is associated with digestive disorders in humans, while glycerol-3-phosphate dehydrogenase 2 (GPD2) encodes a protein on the inner mitochondrial membrane, which can regulate glucose utilization and fatty acid and triglyceride synthesis. The extensive expression and co-expression of these genes across diverse tissues indicate the complex regulation of FE in cattle. Our study provides insight into the epigenetic basis of RFI and gene targets to improve FE in dairy cattle.

Estimating breeding values for feed efficiency in dairy cattle by regression on expected feed intake.

The efficiency with which a dairy cow utilises feed for the various physiological and metabolic processes can be evaluated by metrics that contrast realised feed intake with expected feed intake. In this study, we presented a new metric - regression on expected feed intake (ReFI). This metric is based on the idea of regressing DM intake (DMI) on expected DMI using a random regression model, where energy requirement formulations are applied for the calculation of expected DMI covariables. We compared this new metric with the metrics residual feed intake (RFI) and genetic residual feed intake (gRFI), by applying them on 18581 feed efficiency records from 654 primiparous Nordic Red dairy cows. We estimated variance components for the three metrics and their respective genetic correlations with intake and production traits. In addition, we examined the phenotypes of superior cows. With ReFI, we estimated for feed efficiency a higher genetic variation (4.7%) and heritability (0.23) compared to applying RFI or gRFI. The ReFI metric was genetically uncorrelated with DMI and negatively correlated within energy-corrected milk (ECM), whereas the RFI metric was genetically positively correlated with DMI and metabolic BW. The gRFI metric was genetically positively correlated with DMI and uncorrelated with energy sink traits. Overall, the estimated SE were large. The ReFI metric resulted in a different ranking of cows compared to those based on RFI or gRFI and was superior in selecting the most efficient animals. When the selection was based on ReFI breeding values, then the 10% most efficient cows produced 12.3% more ECM per unit metabolisable energy intake, whereas the corresponding values were only 4.3 or 5.9% when using RFI or gRFI breeding values, respectively. Based on ReFI, superior cows had also higher milk production, whereas based on RFI or gRFI milk production either decreased or was unaffected, respectively. The superiority of the ReFI metric in selecting efficient cows was due to a better modelling of the expected feed intake. The ReFI metric simplified modelling of feed utilisation efficiency in dairy cattle and resulted in breeding values that are equal to percentages of feed saved.

Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle

Context Feed is the largest expense on a dairy farm, therefore improving feed efficiency is important. Recording dry-matter intake (DMI) is a prerequisite for calculating feed efficiency. Genetic variation of feed intake and feed efficiency varies across lactation stages and parities. DMI is an expensive and difficult-to-measure trait. This raises the question of which time periods during lactation would be most appropriate to measure DMI. Aims The aim was to evaluate whether sequence variants selected from genome-wide association studies (GWAS) for DMI recorded at multiple lactation time periods and parities would increase the accuracy of genomic estimated breeding values (GEBVs) for DMI and residual feed intake (RFI). Methods Data of 2274 overseas lactating cows were used for the GWAS to select sequence variants. GWAS was performed using the average of the DMI phenotypes in a 30-day window of six different time periods across the lactation. The most significant sequence variants were selected from the GWAS at each time period for either first or later parities. GEBVs for DMI and RFI in Australian lactating cows were estimated using BayesRC with 50 k single nucleotide polymorphisms (SNPs) and selected GWAS sequence variants. Key results There were differences in DMI genomic correlations and heritabilities between first and later parities and within parity across lactation time periods. Compared with using 50 k single-nucleotide polymorphisms (SNPs) only, the accuracy of DMI GEBVs increased by up to 11% by using the 50 k SNPs plus the selected sequence variants. Compared with DMI, the increase in accuracy for RFI was lower (by 6%) likely because the sequence variants were selected from GWAS for DMI not RFI. The accuracies for DMI and RFI GEBVs were highest by using selected sequence variants from the DMI GWAS in the mid- to late-lactation periods in later parity. Conclusions Our results showed that DMI phenotypes in late lactation time periods could capture more genetic variation and increase genomic prediction accuracy through the use of custom genotype panels in genomic selection. Implications Collecting DMI at the optimal time period(s) of lactation may help develop more accurate and cost-effective breeding values for feed efficiency in dairy cattle.

Taxonomic and predicted functional signatures reveal linkages between the rumen microbiota and feed efficiency in dairy cattle raised in tropical areas.

Ruminants digest plant biomass more efficiently than monogastric animals due to their symbiotic relationship with a complex microbiota residing in the rumen environment. What remains unclear is the relationship between the rumen microbial taxonomic and functional composition and feed efficiency (FE), especially in crossbred dairy cattle (Holstein x Gyr) raised under tropical conditions. In this study, we selected twenty-two F1 Holstein x Gyr heifers and grouped them according to their residual feed intake (RFI) ranking, high efficiency (HE) (n = 11) and low efficiency (LE) (n = 11), to investigate the effect of FE on the rumen microbial taxa and their functions. Rumen fluids were collected using a stomach tube apparatus and analyzed using amplicon sequencing targeting the 16S (bacteria and archaea) and 18S (protozoa) rRNA genes. Alpha-diversity and beta-diversity analysis revealed no significant difference in the rumen microbiota between the HE and LE animals. Multivariate analysis (sPLS-DA) showed a clear separation of two clusters in bacterial taxonomic profiles related to each FE group, but in archaeal and protozoal profiles, the clusters overlapped. The sPLS-DA also revealed a clear separation in functional profiles for bacteria, archaea, and protozoa between the HE and LE animals. Microbial taxa were differently related to HE (e.g., Howardella and Shuttleworthia) and LE animals (e.g., Eremoplastron and Methanobrevibacter), and predicted functions were significatively different for each FE group (e.g., K03395-signaling and cellular process was strongly related to HE animals, and K13643-genetic information processing was related to LE animals). This study demonstrates that differences in the rumen microbiome relative to FE ranking are not directly observed from diversity indices (Faith's Phylogenetic Diversity, Pielou's Evenness, Shannon's diversity, weighted UniFrac distance, Jaccard index, and Bray-Curtis dissimilarity), but from targeted identification of specific taxa and microbial functions characterizing each FE group. These results shed light on the role of rumen microbial taxonomic and functional profiles in crossbred Holstein × Gyr dairy cattle raised in tropical conditions, creating the possibility of using the microbial signature of the HE group as a biological tool for the development of biomarkers that improve FE in ruminants.

Associations of Wearable Sensor Measures With Feed Intake, Production Traits, Lactation, and Environmental Parameters Impacting Feed Efficiency in Dairy Cattle

Feed efficiency is an important trait to dairy production because of its impact on sustainability and profitability. Measuring individual cow feed intake on commercial farms would be unfeasibly costly at present. Thus, developing cheap and portable indicators of feed intake would be highly beneficial for genetic selection and precision feeding management tools. Given the growing use of automated sensors on dairy farms, the objective of this study was to determine the relationship between measurements recorded from multiple wearable sensors and feed intake. A total of three different wearable sensors were evaluated for their association with dry mater intake (DMI). The sensors measured activity (sensors = 3), rumination (sensors = 1), ear temperature (sensors = 1), rumen pH (sensors = 1) and rumen temperature (sensors = 1). A range of 56–340 cows with assorted sensors from 24 to 313 days in milk (DIM) were modeled to evaluate associations with DIM, parity, and contemporary group (CG; comprised of pen and study cohort). Models extending upon these variables included known energy sinks (i.e., milk production, milk fat/protein and metabolic body weight), to characterize the association of sensors measures and DMI. Statistically significant (i.e., P < 0.05) regression coefficients for individual sensor measures with DMI ranged from 9.01E-07 to −3.45 kg DMI/day. When integrating all measures from a single sensor in a model, estimated regression coefficients ranged 8.83E-07 to −3.48 kg DMI/day. Significant associations were also identified for milk production traits, parity, DIM and CG. Associations tended to be highest for timepoints around the time of feeding and when multiple measurements within a sensor were integrated in a single model. The findings of this study indicate sensor measures are associated with feed intake and other energy sink traits and variables impacting feed efficiency. This information would be helpful to improve feed and feeding efficiency on commercial farms as proxy measurements for feed intake.

Integrated meta-omics reveals new ruminal microbial features associated with feed efficiency in dairy cattle

BackgroundAs the global population continues to grow, competition for resources between humans and livestock has been intensifying. Increasing milk protein production and improving feed efficiency are becoming increasingly important to meet the demand for high-quality dairy protein. In a previous study, we found that milk protein yield in dairy cows was associated with the rumen microbiome. The objective of this study was to elucidate the potential microbial features that underpins feed efficiency in dairy cows using metagenomics, metatranscriptomics, and metabolomics.ResultsComparison of metagenomic and metatranscriptomic data revealed that the latter was a better approach to uncover the associations between rumen microbial functions and host performance. Co-occurrence network analysis of the rumen microbiome revealed differential microbial interaction patterns between the animals with different feed efficiency, with high-efficiency animals having more and stronger associations than low-efficiency animals. In the rumen of high-efficiency animals, Selenomonas and members of the Succinivibrionaceae family positively interacted with each other, functioning as keystone members due to their essential ecological functions and active carbohydrate metabolic functions. At the metabolic level, analysis using random forest machine learning suggested that six ruminal metabolites (all derived from carbohydrates) could be used as metabolic markers that can potentially differentiate efficient and inefficient microbiomes, with an accuracy of prediction of 95.06%.ConclusionsThe results of the current study provided new insights into the new ruminal microbial features associated with feed efficiency in dairy cows, which may improve the ability to select animals for better performance in the dairy industry. The fundamental knowledge will also inform future interventions to improve feed efficiency in dairy cows.2fNfKsiaKwf1Ab-9aEYLYCVideo .

Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle.

Simple SummarySensors, routinely collected on-farm tests, and other repeatable, high-throughput measurements can provide novel phenotype information on a frequent basis. Information from these sensors and high-throughput measurements could be harnessed to monitor or predict individual dairy cow feed intake. Predictive algorithms would allow for genetic selection of animals that consume less feed while producing the same amount of milk. Improved monitoring of feed intake could reduce the cost of milk production, improve animal health, and reduce the environmental impact of the dairy industry. Moreover, data from these information sources could aid in animal management (e.g., precision feeding and health detection). In order to implement tools, the relationship of measurements with feed intake needs to be established and prediction equations developed. Lastly, consideration should be given to the frequency of data collection, the need for standardization of data and other potential limitations of tools in the prediction of feed intake. This review summarizes measurements of feed efficiency, factors that may impact the efficiency and feed consumption of an animal, tools that have been researched and new traits that could be utilized for the prediction of feed intake and efficiency, and prediction equations for feed intake and efficiency presented in the literature to date.Feed for dairy cattle has a major impact on profitability and the environmental impact of farms. Sustainable dairy production relies on continued improvement in feed efficiency as a way to reduce costs and nutrient loss from feed. Advances in breeding, feeding and management have led to the dilution of maintenance energy and thus more efficient dairy cattle. Still, many additional opportunities are available to improve individual animal feed efficiency. Sensing technologies such as wearable sensors, image-based and high-throughput phenotyping technologies (e.g., milk testing) are becoming more available on commercial farm. The application of these technologies as indicator traits for feed intake and efficiency related traits would be advantageous to provide additional information to predict and manage feed efficiency. This review focuses on precision livestock technologies and high-throughput phenotyping in use today as well as those that could be developed in the future as possible indicators of feed intake. Several technologies such as milk spectral data, activity, rumen measures, and image-based phenotypes have been associated with feed intake. Future applications will depend on the ability to repeatably measure and calibrate these data across locations, so that they can be integrated for use in predicting and managing feed intake and efficiency on farm.

Genetic analyses of metabolic body weight, carcass weight and body conformation traits in Nordic dairy cattle

Improving feed efficiency in dairy cattle by animal breeding has started in the Nordic countries. One of the two traits included in the applied Saved feed index is called maintenance and it is based on the breeding values for metabolic BW (MBW). However, BW recording based on heart girth measurements is decreasing and recording based on scales is increasing only slowly, which may weaken the maintenance index in future. Therefore, the benefit of including correlated traits, like carcass weight and conformation traits, is of interest. In this study, we estimated genetic variation and genetic correlations for eight traits describing the energy requirement for maintenance in dairy cattle including: first, second and third parity MBW based on heart girth measurements, carcass weight (CARW) and predicted MBW (pMBW) based on predicted slaughter weight, and first parity conformation traits stature (ST), chest width (CW) and body depth (BD). The data consisted of 21329 records from Finnish Ayrshire and 9780 records from Holstein cows. Heritability estimates were 0.44, 0.53, 0.56, 0.52, 0.54, 0.60, 0.17 and 0.26 for MBW1, MBW2, MBW3, CARW, pMBW, ST, CW and BD, respectively. Estimated genetic correlations among MBW traits were strong (>0.95). Genetic correlations between slaughter traits (CARW and pMBW) and MBW traits were higher (from 0.77 to 0.90) than between conformation and MBW traits (from 0.47 to 0.70). Our results suggest that including information on carcass weight and body conformation as correlated traits into the maintenance index is beneficial when direct BW measurements are not available or are difficult or expensive to obtain.

An alternative interpretation of residual feed intake by phenotypic recursive relationships in dairy cattle

There has been increasing interest in residual feed intake (RFI) as a measure of net feed efficiency in dairy cattle. Residual feed intake phenotypes are obtained as residuals from linear regression encompassing relevant factors (i.e., energy sinks) to account for body tissue mobilization. By rearranging the single-trait linear regression, we showed a causal RFI interpretation underlying the linear regression for RFI. It postulates recursive effects in energy allocation from energy sinks on dry matter intake, but the feedback or simultaneous effects are nonexistent. A Bayesian recursive structural equation model was proposed for directly predicting RFI and energy sinks and estimating relevant genetic parameters simultaneously. A simplified Markov chain Monte Carlo algorithm was described. The recursive model is asymptotically equivalent to one-step linear regression for RFI, yet extends the analytical capacity to multiple-trait analysis.

Associations between feed efficiency and aspects of lactation curves in primiparous Holstein dairy cattle

Genetic selection for improved feed efficiency in dairy cattle has received renewed attention over the last decade to address the needs of a growing global population. As milk yield is a critical component of feed efficiency metrics in dairy animals, our objective was to evaluate the associations between feed efficiency in primiparous Holstein cattle and parameters of a mathematical model describing individual lactation curves. The Dijkstra lactation curve model was fit to individual lactation records from 34 Holstein heifers with previously estimated measures of feed efficiency. We found that the optimal fit of the Dijkstra model was achieved using daily milk yield records up to 21 d in milk to capture the rise to peak milk yield and using monthly dairy herd improvement records for the remainder of lactation to accurately characterize lactation persistency. In the period of lactation before peak milk yield, improved feed efficiency was associated with a faster increase in daily milk yield over a shorter period of time at the expense of increased mobilization of body reserves; this serves to reinforce the concept that dairy cattle are primarily capital breeders versus income breeders. Feed efficiency in the period following peak lactation, as measured by gross feed efficiency, return over feed costs, and net energy efficiency of lactation, was positively associated with higher peak milk yield. The findings in early lactation suggest that estimates of feed efficiency could be improved by evaluating feed efficiency relative to conception, rather than parturition and lactation, to better account for the energy stored and released from body reserves in capital breeding.

Supplementation of lysolecithin in milk replacer for Holstein dairy calves: Effects on growth performance, health, and metabolites

Lysolecithin is an antiinflammatory emulsifier associated with improved apparent digestibility of total dietary fat and improved feed efficiency in dairy cattle. However, it is unknown if lysolecithin (LYSO) improves performance in calves. Moreover, since many conventional milk replacers use vegetable-sourced fat (e.g., palm oil), nutrient absorption and fecal score may be affected in neonatal calves. Thus, the objective of this study was to evaluate the effects of LYSO supplemented in milk replacer on performance, metabolites, and gut health of preweaned dairy calves. Holstein calves (n = 32) with adequate passive transfer were assigned in pairs (16 blocks) balanced by birth weight, date of birth, and sex at 1 d of age to randomly receive either LYSO (mixed in 2 milk replacer feedings at a rate of 4 g/d Lysoforte, Kemin Industries Inc., Des Moines, IA) or a milk replacer control (nothing added). Both treatments were fed 6 L/d milk replacer [22.5% crude protein, 16.2% crude fat (vegetable oil fat source) on a dry matter basis with 14% solids] by bucket in 2 daily feedings for 56 d. Calves were individually housed in wooden hutches and offered a commercial calf starter (24.6% crude protein and 13.9% neutral detergent fiber) and water by bucket ad libitum. Feed refusals and calf health was assessed daily. Weights and blood metabolites (glucose, total serum protein, albumin, creatinine, triglycerides, and cholesterol) were sampled weekly, and calves completed the study before weaning at 56 d of age. The effects of LYSO on calf average daily gain, feed efficiency, and blood metabolites were evaluated using a linear mixed model with time as a repeated measure, calf as the subject, and block as a random effect in SAS (SAS Institute Inc., Cary, NC). The effect of LYSO to improve the odds of abnormal fecal score was evaluated using a logistic model. Supplementation of LYSO increased average daily gain (control 0.28 ± 0.03 kg; LYSO 0.37 ± 0.03 kg; least squares means ± standard error of the mean) and increased feed efficiency (gain-to-feed; control 0.25 ± 0.03; LYSO 0.32 ± 0.03). Similarly, LYSO calves had a higher final body weight at d 56 (control 52.11 ± 2.33 kg; LYSO 56.73 ± 2.33 kg). Interestingly, total dry matter intake was not associated with LYSO despite improved average daily gain (total dry matter intake control 1,088.7 ± 27.62 g; total dry matter intake LYSO 1,124.8 ± 27.62 g). Blood glucose, albumin, creatinine, triglycerides, and cholesterol were not associated with LYSO. Indeed, only total serum protein had a significant interaction with LYSO and age at wk 5 and 6. Moreover, control calves had a 13.57 (95% confidence interval: 9.25-19.90) times greater odds of having an abnormal fecal score on any given day during the diarrhea risk period from d 1 to 28. The inclusion of LYSO as an additive in milk replacer in a dose of 4 g/d may improve performance, and calf fecal score, preweaning. Further research should investigate the mechanisms behind the effects of LYSO on fat digestibility in calves fed 6 L/d of milk replacer with vegetable-sourced fat.

Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing

The identification of functional genetic variants and associated candidate genes linked to feed efficiency may help improve selection for feed efficiency in dairy cattle, providing economic and environmental benefits for the dairy industry. This study used RNA-sequencing data obtained from liver tissue from 9 Holstein cows [n = 5 low residual feed intake (RFI), n = 4 high RFI] and 10 Jersey cows (n = 5 low RFI, n = 5 high RFI), which were selected from a single population of 200 animals. Using RNA-sequencing, 3 analyses were performed to identify: (1) variants within low or high RFI Holstein cattle; (2) variants within low or high RFI Jersey cattle; and (3) variants within low or high RFI groups, which are common across both Holstein and Jersey cattle breeds. From each analysis, all variants were filtered for moderate, modifier, or high functional effect, and co-localized quantitative trait loci (QTL) classes, enriched biological processes, and co-localized genes related to these variants, were identified. The overlapping of the resulting genes co-localized with functional SNP from each analysis in both breeds for low or high RFI groups were compared. For the first two analyses, the total number of candidate genes associated with moderate, modifier, or high functional effect variants fixed within low or high RFI groups were 2,810 and 3,390 for Holstein and Jersey breeds, respectively. The major QTL classes co-localized with these variants included milk and reproduction QTL for the Holstein breed, and milk, production, and reproduction QTL for the Jersey breed. For the third analysis, the common variants across both Holstein and Jersey breeds, uniquely fixed within low or high RFI groups were identified, revealing a total of 86,209 and 111,126 functional variants in low and high RFI groups, respectively. Across all 3 analyses for low and high RFI cattle, 12 and 31 co-localized genes were overlapping, respectively. Among the overlapping genes across breeds, 9 were commonly detected in both the low and high RFI groups (INSRR, CSK, DYNC1H1, GAB1, KAT2B, RXRA, SHC1, TRRAP, PIK3CB), which are known to play a key role in the regulation of biological processes that have high metabolic demand and are related to cell growth and regeneration, metabolism, and immune function. The genes identified and their associated functional variants may serve as candidate genetic markers and can be implemented into breeding programs to help improve the selection for feed efficiency in dairy cattle.

Predicted Feed Efficiency index applied to Italian Holstein Friesian cattle population

Submitted 2020-08-02 | Accepted 2020-09-21 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.326-330 Feed efficiency has a major influence on farm profitability and environmental stewardship in the dairy industry. The aim of this study was to describe a new selection index adopted by the Italian Holstein and Jersey Association (ANAFIJ, Cremona, Italy) to improve feed efficiency using data recorded by the official dairy recording system. Predicted dry matter intake (pDMI) was derived from milk yield, fat content, and estimated cow body weight. Fat-protein corrected milk (FPCM) was derived from milk yield corrected for fat, protein, and a fixed coefficient for lactose content (4.80%). Therefore, the predicted feed efficiency (pFE) was estimated as ratio between FPCM and pDMI. Average pFE was 1.27±0.18 (kg.d -1 ) with heritability of 0.32. Predicted Feed Efficiency index (pFEi), traditional and genomic, has been implemented in the Italian Holstein Friesian evaluation system. Results suggest that pFEi may be a new breeding objective for Italian Friesians. The official selection index (PFT), in use since 2002, is positively correlated with pFEi. However, the introduction of pFEi will improve the positive feed efficiency trend. This approach will permit the Italian Holstein Friesian breeders to improve feed efficiency, without increasing costs of recording system. However, to avoid the risk of selecting animals with an excessive negative energy balance after calving, it would be useful to include in the pFE a correction for body condition score and reproductive performances. Meanwhile, in order to increase the accuracy of the predicted phenotype, an Italian consortium is creating a consistent phenotypic critical mass of individual data for dry matter intake in cows, heifers and young bulls. Keywords: feed efficiency, cattle breeding, dry matter intake, breeding value estimation References Cassandro, M. (2020). Animal breeding and climate change, mitigation and adaptation. Journal of Animal Breeding and Genetics, 137(2), 121-122. https://doi.org/10.1111/jbg.12469 Cassandro, M. et al. (2010). Genetic parameters of predicted methane production in Holstein Friesian cows. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany. Cassandro, M., Mele, M. and Stefanon, B. (2013). Genetic aspects of enteric methane emission in livestock ruminants. Italian Journal of Animal Science, 12(3), 450-458. De Haas, Y. et al. (2012). Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. Journal of Dairy Science, 95(10), 6103-6112. De Vries, M. and Veerkamp, R. (2000). Energy balance of dairy cattle in relation to milk production variables and fertility. Journal of Dairy Science, 83(1), 62-69. Finocchiaro, R. et al. (2017). Body weight prediction in Italian Holstein cows. ICAR Technical Series, 22, 95-98. Hegarty, R. et al. (2007). Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Science, 85(6), 1479-1486. Hurley, A. M. et al. (2018). Characteristics of feed efficiency within and across lactation in dairy cows and the effect of genetic selection. Journal of Dairy Science, 101(2), 1267-1280. https://doi.org/https://doi.org/10.3168/jds.2017-12841 Meuwissen, T. H., Hayes, B. J. and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829. National Research Council. (2001). Nutrient Requirements of Dairy Cattle: Seventh Revised Edition, 2001. The National Academies Press. http://www.nap.edu/catalog.php?record_id=9825 Pryce, J. et al. (2014). Genomic selection for feed efficiency in dairy cattle. Animal, 8(1), 1-10. Sjaunja, L. et al. (1990). A Nordic proposal for an energy corrected milk formula. Proceedings of the 2nd Session of Committee for Recording and Productivity of Milk Animal, Paris, p. 156. Veerkamp, R. et al. (2000). Genetic correlation between days until start of luteal activity and milk yield, energy balance, and live weights. Journal of Dairy Science, 83(3), 577-583. Verbyla, K. et al. (2010). Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information. Journal of Dairy Science, 93(6), 2757-2764. Wall, E., Coffey, M. and Brotherstone, S. (2007). The relationship between body energy traits and production and fitness traits in first-lactation dairy cows. Journal of Dairy Science, 90(3), 1527-1537. Wallace, R. J. et al. (2019). A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Science Advances, 5(7), eaav8391.

Mitochondrial protein gene expression and the oxidative phosphorylation pathway associated with feed efficiency and energy balance in dairy cattle

Feed efficiency and energy balance are important traits underpinning profitability and environmental sustainability in animal production. They are complex traits, and our understanding of their underlying biology is currently limited. One measure of feed efficiency is residual feed intake (RFI), which is the difference between actual and predicted intake. Variation in RFI among individuals is attributable to the metabolic efficiency of energy utilization. High RFI (H_RFI) animals require more energy per unit of weight gain or milk produced compared with low RFI (L_RFI) animals. Energy balance (EB) is a closely related trait calculated very similarly to RFI. Cellular energy metabolism in mitochondria involves mitochondrial protein (MiP) encoded by both nuclear (NuMiP) and mitochondrial (MtMiP) genomes. We hypothesized that MiP genes are differentially expressed (DE) between H_RFI and L_RFI animal groups and similarly between negative and positive EB groups. Our study aimed to characterize MiP gene expression in white blood cells of H_RFI and L_RFI cows using RNA sequencing to identify genes and biological pathways associated with feed efficiency in dairy cattle. We used the top and bottom 14 cows ranked for RFI and EB out of 109 animals as H_RFI and L_RFI, and positive and negative EB groups, respectively. The gene expression counts across all nuclear and mitochondrial genes for animals in each group were used for differential gene expression analyses, weighted gene correlation network analysis, functional enrichment, and identification of hub genes. Out of 244 DE genes between RFI groups, 38 were MiP genes. The DE genes were enriched for the oxidative phosphorylation (OXPHOS) and ribosome pathways. The DE MiP genes were underexpressed in L_RFI (and negative EB) compared with the H_RFI (and positive EB) groups, suggestive of reduced mitochondrial activity in the L_RFI group. None of the MtMiP genes were among the DE MiP genes between the groups, which suggests a non-rate limiting role of MtMiP genes in feed efficiency and warrants further investigation. The role of MiP, particularly the NuMiP and OXPHOS pathways in RFI, was also supported by our gene correlation network analysis and the hub gene identification. We validated the findings in an independent data set. Overall, our study suggested that differences in feed efficiency in dairy cows may be linked to differences in cellular energy demand. This study broadens our knowledge of the biology of feed efficiency in dairy cattle.

Bayesian estimation of genetic variance and response to selection on linear or ratio traits of feed efficiency in dairy cattle

This study aimed to estimate genetic parameters of the linear trait genetic residual feed intake (RFI) and the ratio traits feed conversion ratio (FCR) and feed conversion efficiency (FCE) along with dry matter intake (DMI) and energy sink traits such as energy-corrected milk (ECM), body weight (BW), body condition score (BCS), and BW change (BWC) across different weeks in the first lactation of Danish Holstein cows. A second objective was to conduct a Bayesian analysis of direct and correlated superiority of the selected group when selecting on genetic RFI, FCR, or FCE. Feed intake and energy sink traits were recorded during wk 1 to 44 of lactation on 847 primiparous Danish Holstein cows. A Bayesian multivariate random regression animal model was used to analyze DMI, ECM, BW, and BCS in different weeks of lactation. Genetic RFI was obtained by conditioning DMI on ECM, BW, BCS, and BWC using genetic partial regression coefficients. The posterior distribution of the breeding values for FCR and FCE was derived from the posterior distribution of functions of "fixed" environmental effects and random additive genetic effects on DMI and ECM. Genetic superiority of the selected group was defined as the difference in additive genetic mean of the selected top individuals expected to be potential parents, and the total population after integrating genetic trends out of the posterior distribution of selection responses. Posterior means of heritability of genetic RFI ranged from 0.10 to 0.15, genetic variance of FCR and FCE ranged from 2.13 × 10-3 to 3.2 × 10-3 (kg2 DMI/kg2 ECM) and 6.11 × 10-3 to 2.4 × 10-2 (kg2 ECM/kg2 DMI), respectively. Selection against RFI showed a direct response of -1.01 to -2.23 kg/d RFI and correlated responses of -0.031 to -0.056 kg/kg for FCR, 0.104 to 0.160 kg/kg for FCE, and -0.316 to -1.057 kg/d for DMI in different weeks of lactation. Selection against RFI had no significant effect on production traits but selection for ratio traits reduced BW and BCS. Posterior means of genetic correlation between DMI and ratio traits were low. In conclusion, the Bayesian procedure allowed us to estimate genetic RFI without the need for separate multiple regression analysis and considered the non-normal posterior distribution of ratio traits. Selection against genetic RFI might be an effective means to improve feed efficiency compared with ratio traits for feed efficiency in dairy cattle.

Phenotypic modeling of residual feed intake using physical activity and methane production as energy sinks

Residual feed intake (RFI) is a measure of feed efficiency in dairy cattle. This study modeled phenotypic RFI of first- and second-parity Holstein and Jersey dairy cows within 9 lactation segments (consecutive segments of 4 wk each) covering the first 36 lactation weeks. We aimed to evaluate physical activity and daily methane production as additional energy sinks in the estimation of RFI, to examine the correlations of RFI among the first 36 wk of lactation (WOL), and to evaluate whether parities and breeds show similar results. Records for first-parity Holstein (n = 449), second-parity Holstein (n = 298), first-parity Jersey (n = 195), and second-parity Jersey cows (n = 146) were used. Model 1 included the following energy sinks: energy-corrected milk yield, metabolic body weight (BW), body condition score (BCS), daily changes in BW (ΔBW) and BCS (ΔBCS), and physical activity. Model 2 was based on a subset of the data and only for Holstein cows, and included the same energy sinks as Model 1, plus daily methane production. The trajectories of segment-specific partial regression coefficients (PRC) of DMI on activity were similar across parities but differed slightly between breeds. For daily methane production, the trajectory in PRC decreased over lactation segments for first- and second-parity Holstein cows. The trajectories in PRC of DMI on energy-corrected milk yield, metabolic BW, BCS, and ΔBW were generally similar across parities, except for ΔBCS. Activity accounted for on average 7.3, 6.8, 7.2, and 6.4% of DMI for first-parity Holsteins, second-parity Holsteins, first-parity Jerseys, and second-parity Jerseys, respectively. Methane losses accounted for 8.7% and 8.5% of DMI for first- and second-parity Holstein cows, respectively. Repeatability estimates for RFI over 36 WOL for Model 1 were 0.63 for first-parity Holsteins, 0.65 for second-parity Holsteins, 0.76 for first-parity Jerseys, and 0.80 for second-parity Jerseys. For Model 2, the estimates were 0.59 and 0.61 for first- and second-parity Holstein cows, respectively. Correlations of RFI between WOL varied in strength, with weak correlations for the first 2 to 3 WOL with other WOL. In conclusion, physical activity and daily methane production accounted for part of DMI, and RFI of dairy cattle is not the same trait throughout lactation.

Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle

The economic importance of genetically improving feed efficiency has been recognized by cattle producers worldwide. It has the potential to considerably reduce costs, minimize environmental impact, optimize land and resource use efficiency, and improve the overall cattle industry’s profitability. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. The main objective of this review paper is to present an overview of the main genetic and physiological mechanisms underlying feed utilization in ruminants and the process towards implementation of genomic selection for feed efficiency in dairy cattle. In summary, feed efficiency can be improved via numerous metabolic pathways and biological mechanisms through genetic selection. Various studies have indicated that feed efficiency is heritable, and genomic selection can be successfully implemented in dairy cattle with a large enough training population. In this context, some organizations have worked collaboratively to do research and develop training populations for successful implementation of joint international genomic evaluations. The integration of “-omics” technologies, further investments in high-throughput phenotyping, and identification of novel indicator traits will also be paramount in maximizing the rates of genetic progress for feed efficiency in dairy cattle worldwide.

179 Breeding for enhancing feed efficiency in dairy cattle

Abstract Genetically selecting for improved feed efficiency has been recognized by the dairy cattle industry as an important economic and environmental goal. Improved feed efficiency has the potential to significantly reduce costs, improving dairy farmers’ profitability and, at the same time, minimize environmental impact, for example by reducing nutrient loss in manure and methane emissions. Feed efficiency is recognized as a complex trait that may be define in different ways, but it generally describes units of product output per unit of feed required. An overview of genetic selection for improved feed efficiency and international initiatives to implement genomic selection for feed efficiency in dairy cattle is presented. In general, studies have indicated that feed efficiency, defined and assessed in alternative ways, is moderately heritable and genetic selection could be successfully implemented. Various initiatives around the world have worked collaboratively to carried out research and create reference datasets for joint genomic evaluations. An example is the large international Efficient Dairy Genome Project (EDGP) led by Canada. The EDGP database was developed in 2017 to allow data sharing among the international collaborators. Currently, the database contains genotypes and records on feed intake of 5,289 cows and on methane emissions of 1,337 cows from eight research herds in six countries (Australia, Canada, Denmark, Switzerland, United Kingdom and United States). Genetic parameters (heritability and genetic correlations) were estimated for dry matter intake, metabolic body weight and energy corrected milk at two time-periods: a) 5–60 DIM and b) 60–150 DIM. These parameters provide a basis for development of breeding value estimation procedures and subsequent selection index for feed efficiency, which will incorporate genomic information.

PSVIII-19 Meta-analysis of genetic parameter estimates for feed efficiency traits in dairy cattle

Abstract Over 60% of the production costs in dairy cattle are related to feeding. Genetic selection for improved feed efficiency (FE) is a very promising alternative to reduce feeding costs, increase the industry profitability, and reduce the industry environmental footprints. The success of genetic selection relies in part on the heritability (h2) of the indicator traits included in the breeding programs. Several studies have reported h2 estimates for different FE indicator traits, with a broad range of estimates. To obtain more consistent h2 estimates across studies and populations, we performed a meta-analysis of published h2 estimates using four different groups of FE indicator traits commonly used in dairy cattle: 1) energy intake (EI); 2) residual feed intake (RFI); 3) feed (dry-matter) intake (FI); and 4) feed conversion efficiency (FCE). A comprehensive literature review identified 148 h2 estimates across 39 scientific papers from 13 different countries, published between 1991 and 2019. Thereafter, a meta-analysis based on random-effects model was used to summarize and address the variability of the parameter estimates. Our study confirmed that FE indicator traits in dairy cattle are under moderate genetic control. The h2 estimates were 0.18±0.02, 0.19±0.02, 0.29±0.01, and 0.19±0.03 for EI, RFI, FI, and FCE, respectively. In addition, our findings showed that h2 estimates for FE indicator traits in different studies have significant heterogeneity (I2 index estimated for EI, RFI, FI and FCE was 80.5%, 59.8%, 81.7%, and 55.7%, respectively). Among the possible sources of variation that contributed to the heterogeneity across studies are country, type of housing, life stage, and diet. The results reported here summarize the overall level of genetic control of FE in dairy cattle, which are useful for genetic evaluations when reliable h2 estimates for FE are not available in the studied dairy cattle population.

PSXIV-38 Short-term adaptation of milk yield and feed efficiency to individualized dietary changes

Abstract Automated in-parlor feeding and other precision individual animal feeding strategies are a primary interest of efforts to enhance precision animal nutrition. Our objective was to determine short-term effects of changes in diet composition on milk yield and feed efficiency in dairy cattle. A replicated latin square with 4 groups of 6 cows was used to evaluate responses to feeding increasing amounts of ground corn (GC), soybean meal (SBM), chopped grass hay (GH), or total mixed ration (TMR) for 9 d periods. Throughout each period, treatments were top dressed at incrementally increasing rates to provide 0 to 20 percent of calculated net energy intake. Cows were group housed and fed once daily in Calan gates for 36 days. Mean milk yield, efficiency, and the linear slopes of these responses to top dress intake were analyzed during each 9 day period. Mean responses and response slopes were analyzed using a mixed-effect model with fixed effects for top dress and random effects for animal, period, and square. Milk yield was significantly affected by top dress type (P = 0.010) and GH differed significantly from SBM (P = 0.015). However, feed efficiency did not differ with top dress type. The milk yield slope was not affected by top dress type (P = 0.268), but the efficiency slope was affected (P = 0.011). The efficiency response to dietary changes was greater for added SBM than for added GH (P = 0.012), but SBM responses did not differ from GC (P = 0.484) or TMR (P = 0.772). These results suggest opportunity to use individualized, automated feeding strategies to adjust dietary composition in the short-term to target economically optimal formulations without sacrificing production.